构建可控制复杂度的时空点过程评估基准，精准诊断模型弱点。
HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity
- 基于多变量霍克斯模型设计四维复杂度轴
- 实测基线模型在高耦合复杂度下性能显著下降
- 适合研究时空建模与模型诊断的学者使用
现有时空点过程（STPP）模型评估依赖于结构未知的真实数据集，导致模型失败难以归因。本文提出HawkesNest，一种基于多变量霍克斯模型的生成对齐基准，支持可控的时空模式复杂度测试。该基准定义四个复杂度轴：时空纠缠、背景异质性、跨类型交互和领域拓扑，每个轴对应由生成机制确定的确定性指数。在保持全局速率、稳定性及仿真预算不变的前提下，通过调节各轴可实现对模型的诊断性压力测试。我们验证了各指数在受控扫描下具有单调性且近似正交。实验表明，尽管霍克斯类基线模型与生成机制结构一致，但在异质性与纠缠联合复杂度下仍显著退化；同时揭示神经模型对时空纠缠的敏感性——AutoSTPP在仅增加纠缠时仍表现脆弱。代码已开源。
原文摘要 · Abstract (English)
Evaluation of spatiotemporal point process (STPP) models relies heavily on opaque real-world datasets, where latent generative structure is unknown and model failures are difficult to attribute. We introduce HawkesNest, a generator-aligned benchmark for controlled spatiotemporal pattern complexity built on a multivariate Hawkes backbone. HawkesNest defines four complexity axes: space--time entanglement, background heterogeneity, cross-type interaction, and domain topology. Each axis is associated with a deterministic index computed from the latent data-generating mechanism. By varying these axes while holding global rate, stability, and simulation budget fixed, HawkesNest enables diagnostic stress tests of STPP models under known structural difficulty. We verify that the indices are monotone and nearly orthogonal under controlled sweeps. We illustrate its use by showing that Hawkes-family baselines degrade under joint heterogeneity--entanglement complexity, even though they are structurally aligned with the Hawkes data-generating backbone. We further show that HawkesNest exposes neural-model sensitivity: AutoSTPP remains vulnerable under isolated increases in space--time entanglement. Code. Available at https://github.com/YahyaAalaila/HawkesNest
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